SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3191–3200 of 3304 papers

TitleStatusHype
Thermal Human face recognition based on Haar wavelet transform and series matching technique—0
Some Options for L1-Subspace Signal Processing—0
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy—0
Towards Basque Oral Poetry Analysis: A Machine Learning Approach—0
Learning from the past, predicting the statistics for the future, learning an evolving systemCode0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—0
Manopt, a Matlab toolbox for optimization on manifolds—0
Learning Deep Representation Without Parameter Inference for Nonlinear Dimensionality Reduction—0
High-Dimensional Regression with Gaussian Mixtures and Partially-Latent Response Variables—0
On b-bit min-wise hashing for large-scale regression and classification with sparse data—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified